Prepare RStudio environment for all tasks to follow.
We load the data derived by the script "./reports/data_preparation/dsL_hrs.R"" Variables chosen are age in years, eduction in years, gender, race, if they drink currently, if they smoked ever,
December 5, 2015
Load Packages
Slide 1
Load graph settings for creating figures
Slide 2
Chooses variables for model - End up with model of ~6 or 7 due to conflicting results (seemed reasonable)
Subset selection object
Call: regsubsets.formula(ds12$bmi ~ ., data = ds12, nvmax = 18)
18 Variables (and intercept)
Forced in Forced out
hhidpn FALSE FALSE
conde FALSE FALSE
agey FALSE FALSE
cogtot FALSE FALSE
mstot FALSE FALSE
raedyrs FALSE FALSE
cesd FALSE FALSE
wtresp FALSE FALSE
shltc FALSE FALSE
shltnum2 FALSE FALSE
shltnum3 FALSE FALSE
shltnum4 FALSE FALSE
shltnum5 FALSE FALSE
gendernum2 FALSE FALSE
racenum2 FALSE FALSE
racenum3 FALSE FALSE
smokenum1 FALSE FALSE
drinknum1 FALSE FALSE
1 subsets of each size up to 18
Selection Algorithm: exhaustive
hhidpn conde agey cogtot mstot raedyrs cesd wtresp shltc shltnum2 shltnum3 shltnum4 shltnum5 gendernum2
1 ( 1 ) " " " " "*" " " " " " " " " " " " " " " " " " " " " " "
2 ( 1 ) " " "*" "*" " " " " " " " " " " " " " " " " " " " " " "
3 ( 1 ) " " "*" "*" " " " " " " " " " " " " " " " " " " " " " "
4 ( 1 ) " " "*" "*" " " " " "*" " " " " " " " " " " " " " " " "
5 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " " " " " " " " " " "
6 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " " " " " " " " " " "
7 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " " " " " " " " " "*"
8 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " " " " " "*" " " "*"
9 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " " " "*" "*" " " "*"
10 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " "*" "*" "*" " " "*"
11 ( 1 ) " " "*" "*" "*" " " "*" " " " " " " "*" "*" "*" "*" "*"
12 ( 1 ) " " "*" "*" "*" " " "*" " " " " "*" "*" "*" "*" "*" "*"
13 ( 1 ) " " "*" "*" "*" " " "*" " " " " "*" "*" "*" "*" "*" "*"
14 ( 1 ) " " "*" "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*" "*"
15 ( 1 ) " " "*" "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*" "*"
16 ( 1 ) "*" "*" "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*" "*"
17 ( 1 ) "*" "*" "*" "*" "*" "*" "*" " " "*" "*" "*" "*" "*" "*"
18 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*" "*" "*" "*" "*" "*"
racenum2 racenum3 smokenum1 drinknum1
1 ( 1 ) " " " " " " " "
2 ( 1 ) " " " " " " " "
3 ( 1 ) "*" " " " " " "
4 ( 1 ) "*" " " " " " "
5 ( 1 ) "*" " " " " " "
6 ( 1 ) "*" " " " " "*"
7 ( 1 ) "*" " " " " "*"
8 ( 1 ) "*" " " " " "*"
9 ( 1 ) "*" " " " " "*"
10 ( 1 ) "*" " " " " "*"
11 ( 1 ) "*" " " " " "*"
12 ( 1 ) "*" " " " " "*"
13 ( 1 ) "*" " " "*" "*"
14 ( 1 ) "*" " " "*" "*"
15 ( 1 ) "*" "*" "*" "*"
16 ( 1 ) "*" "*" "*" "*"
17 ( 1 ) "*" "*" "*" "*"
18 ( 1 ) "*" "*" "*" "*"
[1] "np" "nrbar" "d" "rbar" "thetab" "first" "last" "vorder" "tol" [10] "rss" "bound" "nvmax" "ress" "ir" "nbest" "lopt" "il" "ier" [19] "xnames" "method" "force.in" "force.out" "sserr" "intercept" "lindep" "nullrss" "nn" [28] "call"
(Intercept) hhidpn conde agey cogtot mstot raedyrs cesd
3.881552e+01 5.646507e-10 8.072686e-01 -1.895553e-01 7.727136e-02 3.974560e-02 -1.310662e-01 -1.657343e-02
wtresp shltc shltnum2 shltnum3 shltnum4 shltnum5 gendernum2 racenum2
-2.101632e-06 -2.685605e-01 9.923701e-01 1.354036e+00 1.710996e+00 1.421305e+00 -5.128229e-01 1.171970e+00
racenum3 smokenum1 drinknum1
-2.648775e-01 -2.680000e-01 -4.866865e-01
[1] "which" "rsq" "rss" "adjr2" "cp" "bic" "outmat" "obj"
[1] 1
[1] 18
[1] 13
[1] 12
Slide 3
Create Plots of cognition, number of chronic conditions, education in years and age in years by BMI (with gender, if they drank, and race included as factors) 
Slide 4
Creates prediction function for models
Slide 5
Creates models of variables of interest
Analysis of Deviance Table
Model: gaussian, link: identity
Response: bmi
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 9416 294329
conde 1 12985 9415 281344
Analysis of Variance Table Model 1: bmi ~ agey Model 2: bmi ~ poly(agey, 2) Model 3: bmi ~ poly(agey, 3) Model 4: bmi ~ poly(agey, 4) Model 5: bmi ~ poly(agey, 5) Res.Df RSS Df Sum of Sq F Pr(>F) 1 9415 278859 2 9414 278825 1 34.151 1.1529 0.2830 3 9413 278800 1 24.797 0.8371 0.3602 4 9412 278797 1 2.895 0.0977 0.7546 5 9411 278761 1 36.072 1.2178 0.2698
Analysis of Variance Table Model 1: bmi ~ raedyrs Model 2: bmi ~ poly(raedyrs, 2) Model 3: bmi ~ poly(raedyrs, 3) Model 4: bmi ~ poly(raedyrs, 4) Model 5: bmi ~ poly(raedyrs, 5) Res.Df RSS Df Sum of Sq F Pr(>F) 1 9415 292709 2 9414 292521 1 188.573 6.0691 0.01377 * 3 9413 292441 1 79.904 2.5717 0.10883 4 9412 292429 1 11.532 0.3711 0.54240 5 9411 292410 1 19.719 0.6347 0.42567 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table Model 1: bmi ~ cogtot Model 2: bmi ~ poly(cogtot, 2) Model 3: bmi ~ poly(cogtot, 3) Model 4: bmi ~ poly(cogtot, 4) Model 5: bmi ~ poly(cogtot, 5) Res.Df RSS Df Sum of Sq F Pr(>F) 1 9415 293276 2 9414 292787 1 489.14 15.7247 7.38e-05 *** 3 9413 292746 1 41.22 1.3250 0.2497 4 9412 292744 1 2.40 0.0771 0.7812 5 9411 292743 1 1.00 0.0321 0.8579 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Slide 6
Adds models to figures produced above 
Conclusions
Health conditions
Data seem show that the more health conditions you have, the higher your BMI, while people's self assessment of their health corresponds to the amount of health conditions they have (i.e. they assess their health as being worse the more health conditions they have) ###
Age
Data seem show that the older you are, the lower your BMI is, no change between the genders###
Education
Data seem show that the more education you have, the lower your BMI is (only a slight relationship though, not super strong), while people's self assessment of their health does seem to correspond to their eduction, it appears that people with a higher education seem to rate their health as being better ###
Education
Data seem show that the more ###
Race
Race seems to show that
Appendix
Removes all variables except variables conisdered for models